Respiratory rate is one of the important vital signs, and much effort has been centered on extracting it from pulse oximeter and electrocardiogram recordings. The research has been driven largely by the desire to reduce the number of sensors that need to be connected to a patient to obtain vital signs.
Recent promising approaches based on time-frequency spectral techniques have been used to extract respiratory rates directly from a pulse oximeter. With recognition that respiration modulates heart rate and that they are both time-varying, time-frequency analyses were used to extract the former signal. Specifically, the continuous wavelet transform (CWT) and variable frequency complex demodulation (VFCDM) methods were utilized to extract either frequency modulation or amplitude modulation seen in the frequency range associated to the heart rate. Both CWT and VFCDM methods have been shown to provide accurate respiratory rate extraction in the low- and moderate-breathing rates (12-36 breaths/min). However, these time-frequency methods' capability became less reliable with increased respiratory rates.
In a recent work, it has been shown that the high resolution time-frequency analysis of the pulse oximeter signal followed by taking the power spectrum of the extracted frequency modulation signal around the heart rate frequency resulted in the best accuracy among all compared methods, including the time-invariant autoregressive (AR) method. While the AR method was not as accurate as the time-frequency methods, it has many attractive features because it is more computationally efficient and works reasonably well even with short data records. It has been conjectured that one of the key reasons the AR method did not perform as well as other methods was due to an inefficient model order search criterion, namely, its reliance on the Akaike information criterion (AIC). Further, an arbitrary decision regarding the proper choice of the poles and the phase related to the estimated AR coefficients had to be made in order to extract the correct respiratory rate, which can also compromise its accuracy.
There is a need for a more accurate AR method for extracting respiratory rates directly from a pulse oximeter.
Even with a more accurate AR method for extracting respiratory rates directly from a pulse oximeter, as the Signal-to-Noise Ratio (SNR) decreases, the accuracy of the method will be affected. There is a need for an AR based method for extracting respiratory rates directly from a pulse oximeter that produce accurate results at low SNR.